{"url":"/dataset/smot","name":"SMOT","full_name":"Single sequence-Multi Objects Training","description_markdown":"The SMOT dataset, Single sequence-Multi Objects Training, is collected to represent a practical scenario of collecting training images of new objects in the real world, i.e. a mobile robot with an RGB-D camera collects a sequence of frames while driving around a table to learning multiple objects and tries to recognize objects in different locations.\r\n\r\nSource: [SMOT](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/smot/)","description_withheld":null,"homepage":"https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/smot/","introduced_date":"2020-05-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/neural-object-learning-for-6d-pose-estimation","title":"Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images","first_author":"Kiru Park","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"6D Pose Estimation","url":"/task/6d-pose-estimation-1","datasets_with_task":"/datasets/task/6d-pose-estimation-1"}],"languages":[],"variants":["SMOT"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}